With there being many technical and ethical issues with artificial intelligence (AI) that involve marginalized communities, there is a growing interest for design methods used with marginalized people that may be transferable to the design of AI technologies. Participatory design (PD) is a design method that is often used with marginalized communities for the design of social development, policy, IT and other matters and solutions. However, there are issues with the current PD, raising concerns when it is applied to the design of technologies, including AI technologies. This paper argues for the use of PD for the design of AI technologies, and introduces and proposes a new PD, which we call agile participatory design, that not only can could be used for the design of AI and data-driven technologies, but also overcomes issues surrounding current PD and its use in the design of such technologies.

We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robust loss function that encodes our intuition of algorithmic fairness for the ML task at hand. Unlike prior approaches to individual fairness that only work with smooth ML models, our approach also works with non-smooth models such as decision trees. We show that our algorithm converges globally and generalizes. We also demonstrate the efficacy of our algorithm on three ML problems susceptible to algorithmic bias.

These subprocesses are interrelated, with mapping considered to be the pivotal process (Gentner, 1983). Mapping may play a role in retrieval, as mapping a target analog to multiple potential source analogs stored in memory can help identify one or more that seems promising; and the correspondences computed by mapping support subsequent inference and schema induction. Thus, because of its centrality to analogical reasoning, the present paper focuses on the process of mapping between two analogs. We also consider the possible role that mapping may play in analog retrieval. Computational Approaches to Analogy Computational models of analogy have been developed in both artificial intelligence (AI) and cognitive science over more than half a century (for a recent review and critical analysis, see Mitchell, 2021). These models differ in many ways, both in terms of basic assumptions about the constraints that define a "good" analogy for humans, and in the detailed algorithms that accomplish analogical reasoning. For our present purposes, two broad approaches can be distinguished. The first approach, which can be termed representation matching, combines mental representations of structured knowledge about each analog with a matching process that computes some form of relational similarity, yielding a set of correspondences between the elements of the two analogs. The structured knowledge about an analog is typically assumed to approximate the content of propositions expressed in predicate calculus; e.g., the instantiated relation "hammer hits nail" might be coded as hit (hammer, nail).

Recently, there are unprecedented data growth originating from different online platforms which contribute to big data in terms of volume, velocity, variety and veracity (4Vs). Given this nature of big data which is unstructured, performing analytics to extract meaningful information is currently a great challenge to big data analytics. Collecting and analyzing unstructured textual data allows decision makers to study the escalation of comments/posts on our social media platforms. Hence, there is need for automatic big data analysis to overcome the noise and the non-reliability of these unstructured dataset from the digital media platforms. However, current machine learning algorithms used are performance driven focusing on the classification/prediction accuracy based on known properties learned from the training samples. With the learning task in a large dataset, most machine learning models are known to require high computational cost which eventually leads to computational complexity. In this work, two supervised machine learning algorithms are combined with text mining techniques to produce a hybrid model which consists of Na\"ive Bayes and support vector machines (SVM). This is to increase the efficiency and accuracy of the results obtained and also to reduce the computational cost and complexity. The system also provides an open platform where a group of persons with a common interest can share their comments/messages and these comments classified automatically as legal or illegal. This improves the quality of conversation among users. The hybrid model was developed using WEKA tools and Java programming language. The result shows that the hybrid model gave 96.76% accuracy as against the 61.45% and 69.21% of the Na\"ive Bayes and SVM models respectively.

For unsupervised image-to-image translation, we propose a discriminator architecture which focuses on the statistical features instead of individual patches. The network is stabilized by distribution matching of key statistical features at multiple scales. Unlike the existing methods which impose more and more constraints on the generator, our method facilitates the shape deformation and enhances the fine details with a greatly simplified framework. We show that the proposed method outperforms the existing state-of-the-art models in various challenging applications including selfie-to-anime, male-to-female and glasses removal. The code will be made publicly available.

Among the essential body functions like breathing, eating or drinking, sleeping is probably the most problematic one nowadays. According to the US government through its Centers for Control of Disease and Prevention (CDC), about 9 million citizens have frequent problems to develop good quality sleep and end up resorting to sleeping pills (Ford et al. 2014). In parallel, recent studies (Stranges et al. 2012; Chong et al. 2013) have estimated that at least 15% of adult population might have some kind of sleeping problem or poor-quality sleep as a result of a number of issues. Moreover, the World Health Organization (WHO) (2015) claimed that a good quality sleep was one of the most important factors for good health while sleeping problems were directly related to other diseases, including depression, stress or early cardiac diseases. As a consequence, new units focused on the study and treatment of sleeping problems have been created in hospitals all over the world. The physicians in these units have as their main tool for their work the records obtained during their patients' sleep. These records, called polysomnography (PSG), may include a great variety of signals such as Electrocardiograms, Electroencephalograms, respiratory signals or movement records. Among these signals, the most important one is the Electroencephalogram (EEG) because it is the most reliable to determine the sleep stage a patient is in. The interpretation of an EEG is a highly time-consuming activity (Akben and Alkan 2016), which usually requires a specialist and it is deeply dependent on the expert's expertise.

Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives. Empowered by edge computing, AI workloads are migrating from centralized cloud architectures to distributed edge systems, introducing a new paradigm called edge AI. While edge AI has the promise of bringing significant increases in autonomy and intelligence into everyday lives through common edge devices, it also raises new challenges, especially for the development of its algorithms and the deployment of its services, which call for novel design methodologies catered to these unique challenges. In this paper, we provide a comprehensive survey of the latest enabling design methodologies that span the entire edge AI development stack. We suggest that the key methodologies for effective edge AI development are single-layer specialization and cross-layer co-design. We discuss representative methodologies in each category in detail, including on-device training methods, specialized software design, dedicated hardware design, benchmarking and design automation, software/hardware co-design, software/compiler co-design, and compiler/hardware co-design. Moreover, we attempt to reveal hidden cross-layer design opportunities that can further boost the solution quality of future edge AI and provide insights into future directions and emerging areas that require increased research focus.

Sticks and stones may break my bones, but words will never hurt me. This was a mantra I picked up on the playground at elementary school--something I repeated over and over again anytime I came face to face with racism. It was a coping mechanism meant to guard my heart from the cacophony of discriminatory comments that shaped me as a young Korean American girl growing up in predominantly white spaces. But now that I'm well into adulthood, I think about the girls of color who are also being taught to pretend that words don't hurt--and the people this way of thinking actually protects. It's hard to escape the unrelenting consequences of racism: In the past year alone, we lost Breonna Taylor, George Floyd, Ahmaud Arbery, and the six women of Asian descent murdered in Atlanta (Xiaojie "Emily" Tan, Daoyou Feng, Suncha Kim, Yong Ae Yue, Soon Chung Park, Hyun Jung Grant) at the hands of this insidious disease--and those are just the names that were in the headlines. If we don't acknowledge ...

As the realities of climate change take hold across the planet, the risks of natural hazards and disasters are becoming ever more familiar. Meteorologists, aiming to protect increasingly populous countries and communities, are tapping into artificial intelligence (AI) to get them the edge in early detection and disaster relief. This potential was in focus at a recent workshop feeding into the first meeting of the new Focus Group on AI for Natural Disaster Management. The group is open to all interested parties, supported by the International Telecommunication Union (ITU) together with the World Meteorological Organization (WMO) and UN Environment. "AI can help us tackle disasters in development work as well as standardization work. With this new Focus Group, we will explore AI's ability to analyze large datasets, refine datasets and accelerate disaster-management interventions," said Chaesub Lee, Director of the ITU Telecommunication Standardization Bureau, in opening remarks to the workshop.

Neuroscience should be the sexiest of the sciences. To study it is to study the very stuff that makes stuff studiable in the first place. Then you look at an fMRI scan and realize it's all, actually, amazingly boring. This bit lights up when that thing happens--so what? A functional map of the brain tells us almost nothing about what it feels like to be alive. Even certain neuroscientists have an axon to grind with this "objective," "cognitivist" way of thinking.